控制理论(社会学)
控制器(灌溉)
计算机科学
参数统计
弹道
自适应控制
人工神经网络
李雅普诺夫函数
非线性系统
有界函数
控制工程
控制(管理)
工程类
人工智能
数学
数学分析
统计
物理
天文
量子力学
农学
生物
作者
Gan Yu,Joel Reis,Carlos Silvestre
出处
期刊:IEEE robotics and automation letters
日期:2023-05-01
卷期号:8 (5): 2574-2581
被引量:3
标识
DOI:10.1109/lra.2023.3254450
摘要
This letter presents the design and experimental study of an adaptive nonlinear controller for Unmanned Aerial Vehicles (UAVs) in the presence of unknown time-varying disturbances, and model parametric uncertainty. We employ an adaptive Neural Network (NN), used to approximate the partially unknown system, in tandem with a simple controller designed for trajectory tracking, not of the center of mass, but of a point located along the UAV's vertical body axis. This strategy allows: (i) to avoid the two-subsystems control paradigm generally adopted by conventional UAV controllers; (ii) all control inputs to be defined at once; and (iii) to lump all unknown dynamics from both translational and rotational levels into a single vector term. The weights of the NN are determined online by an adaptive law based on the Lyapunov synthesis method. The tracking and adaptation errors are shown to be uniformly ultimately bounded. Simulation and experimental results, including comparison data, are provided to validate and assess the proposed control solution.
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